Patio Temporal Backpropagation Simplifies Spiking Neural.Network Training

Spiking neural networks have shown great potential in mimicking the brain's computational abilities. However, training these networks has been a challenge due to the complex temporal dynamics involved. This paper introduces a novel technique called Patio-Temporal Backpropagation that simplifies the training process by incorporating a simplified temporal model. By using this method, high-performance spiking neural networks can be trained more efficiently.